Poisson learning doesn't solve graph semi-supervised learning issues.
problem Global information loss in graph-based semi-supervised learning.
method Poisson learning is Laplace regularization with thresholding.
result Poisson learning cannot overcome the global information loss problem.
Poisson learning improves graph-based semi-supervised learning at very low label rates.
problem Degeneracy of Laplacian semi-supervised learning at low label rates.
method Replaces label assignment with source and sink placement, solving Poisson equation.
result Provably more stable and informative predictions than Laplacian learning.
Bayesian analysis shows unlabeled data improve graph-based semi-supervised learning.
problem Improving semi-supervised learning with limited labeled data.
method Bayesian nonparametric approach using unlabeled data for graph-based learning.
result Posterior contracts optimally around the truth with sufficient unlabeled data.
Proposes a graph learning framework for clustering and semi-supervised classification.
problem Graph-based clustering and semi-supervised classification techniques have shown impressive performance but lack explicit cluster structure.
method Uses self-expressiveness of samples for global structure and adaptive neighbor approach for local structure. Incorporates rank constraint to ensure optimal performance.
result The proposed method achieves better performance than state-of-the-art methods in clustering and semi-supervised classification.
Graph Random Neural Network improves semi-supervised learning on graphs.
problem Over-smoothing, non-robustness, and weak-generalization in GNNs with few labeled nodes.
method Random propagation strategy and consistency regularization.
result Significantly outperforms state-of-the-art GNN baselines on semi-supervised node classification.
Graph semi-supervised learning classifies points on manifold using variational autoencoders and GNN.
problem Classifying points on low-dimensional manifolds using limited labeled data.
method Model data as points on a manifold, approximate manifold with VAE, construct geometric graph, solve semi-supervised node classification with GNN.
result Generalization gap diminishes with graph size and training procedure, vanishing asymptotically.
Improved graph-based semi-supervised learning with model change active learning.
problem Identifying which unlabelled data points to label to best improve classifier performance.
method Pairing model change active learning with graph-based semi-supervised learning methods.
result Improved multiclass classification performance over prior methods.
Infinitely wide GCNs perform as GPs for graph semi-supervised learning.
problem Graph-based semi-supervised classification with limited labeled data.
method Proposes GPGC model combining GCNs and GPs for semi-supervised learning.
result GPGC outperforms state-of-the-art methods on various datasets.
New approach combines semi-supervised learning and bandits for better predictions.
problem Online semi-supervised learning with bandit feedback for applications like clinical trials and ad recommendations.
method Adjusted Graph Convolutional Network (GCN) for contextual bandits, with semi-supervised missing rewards imputation.
result Developed multi-GCN embedded contextual bandit algorithms verified on real-world datasets.
Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning, as they can exploit the connectivity patterns between labeled and unlabeled data samples to improve learning performance. However, existing graph-based methods either are limited in their ability to jointly …
The quality of a graph is determined jointly by three key factors of the graph: nodes, edges and similarity measure (or edge weights), and is very crucial to the success of graph-based semi-supervised learning (SSL) approaches. Recently, dynamic graph, which means part/all its factors are dynamically updated during the…
The paper extends graph-based semi-supervised learning to infinite-dimensional Wasserstein space.
problem Graph-based semi-supervised learning in high-dimensional data.
method Laplace Learning in the Wasserstein space, proving variational convergence and characterizing the Laplace-Beltrami operator.
result Consistent classification performance in high-dimensional settings.
We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled samples. Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpful for these semi-sup…
A new method improves graph-based learning for high-dimensional data.
problem Inconsistent high-dimensional learning efficiency of semi-supervised graph regularization.
method Introducing a novel regularization approach involving centering operation.
result Empirical results show improved performance over spectral clustering.
GraphFL tackles semi-supervised node classification on graphs using federated learning.
problem Real-world graph-based problems often require collecting the entire graph and labeling a reasonable number of labels, which is impractical and costly.
method GraphFL is a federated learning framework that addresses non-IID data, new label domains, and unlabeled data issues in graph-based semi-supervised node classification.
result GraphFL significantly outperforms compared FL baselines and self-training methods.
The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the credit cards' fraud transactions, data scientists normally employ the unsupervised…
Study how noisy labels affect semi-supervised learning.
problem Effect of noisy labels on semi-supervised learning performance.
method Proposed an algorithm derived from a continuous relaxation of the Maximum A Posteriori (MAP) estimator for a Degree Corrected Stochastic Block Model (DC-SBM).
result Our approach achieves promising performance even with very noisy labeled data.
GNNs improve semi-supervised node regression, but why? We explain.
problem Understanding when and why GNNs succeed in semi-supervised node regression.
method Aggregate-and-readout model encompassing message passing architectures, least-squares estimation over GNNs with linear graph convolutions and a deep ReLU readout.
result Sharp non-asymptotic risk bound separating approximation, stochastic, and optimization errors.
New kernel improves graph learning with fewer labeled data.
problem Limited kernels for node-level problems on graphs.
method Derived from a regularization framework, transductive kernel for graphs with node features.
result Improved learning on fewer training points and non-Euclidean data.
In this work, we address semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures. Recent works often solve this problem via advanced graph convolution in a conventionally supervised manner, but the performance could degrade …
Develops neural network for directed hypergraphs for node classification.
problem Irregular data structure, particularly directed graphs.
method Directed hypergraph neural network and semi-supervised learning method.
result Novel directed hypergraph neural network achieves highest accuracies on node classification tasks.
LC-GNN improves GNNs for node classification by incorporating label consistency.
problem Limited performance of GNNs due to label consistency assumption not always holding.
method LC-GNN uses node pairs with the same label but unconnected to expand GNN's receptive field.
result LC-GNN outperforms traditional GNNs in semi-supervised node classification.
LFGCN uses Levy Flights for graph semi-supervised learning.
problem Semi-supervised learning on graphs with improved performance.
method Lévy Flights into random walks, preferential P-DropEdge method.
result Significant improvement in classification performance, especially for heterogeneous graphs.
To explore underlying complementary information from multiple views, in this paper, we propose a novel Latent Multi-view Semi-Supervised Classification (LMSSC) method. Unlike most existing multi-view semi-supervised classification methods that learn the graph using original features, our method seeks an underlying late…
To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect these irregular trade behaviors in the stock market, data scientists normally employ the supervised learning techniques. In this paper, we empl…
We introduce nonlinear higher-order label spreading for semi-supervised learning.
problem Efficient semi-supervised learning on graphs with complex label spreading.
method We add nonlinearity to label spreading through higher-order graph structures, proving convergence and demonstrating efficiency on various datasets.
result Our nonlinear higher-order label spreading algorithm converges to the global solution and performs favorably compared to classical methods.
Study optimal spectral estimator for semi-supervised node classification.
problem Semi-supervised node classification on CSBM with limited labels.
method Spectral estimator inspired by PCA, graph ridge regression, GCN.
result Achieves information-theoretical threshold for exact recovery.
ASGN uses active semi-supervised learning to predict molecular properties efficiently.
problem Predicting molecular properties with scarce labeled data and high computational cost.
method ASGN combines a teacher-student framework with active learning to handle joint representation and property learning.
result ASGN achieves remarkable performance in property prediction on public datasets.
Optimizes graph neural networks using natural gradient descent.
problem Improving efficiency and performance of graph neural networks.
method Employing natural gradient descent to optimize graph neural networks.
result Natural gradient optimization leads to superior performance compared to existing methods.
This paper studies learning the representations of whole graphs in both unsupervised and semi-supervised scenarios. Graph-level representations are critical in a variety of real-world applications such as predicting the properties of molecules and community analysis in social networks. Traditional graph kernel based me…
EPFGNN models graph connections for better node classification.
problem Graph node classification issues due to feature aggregation.
method EPFGNN models graph as a Markov Random Field with explicit pairwise factors and a GNN backbone.
result EPFGNN improves semi-supervised node classification performance.
We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions.…
Semi-supervised learning algorithms typically construct a weighted graph of data points to represent a manifold. However, an explicit graph representation is problematic for neural networks operating in the online setting. Here, we propose a feed-forward neural network capable of semi-supervised learning on manifolds w…
We describe a computationally efficient, stochastic graph-regularization technique that can be utilized for the semi-supervised training of deep neural networks in a parallel or distributed setting. We utilize a technique, first described in [13] for the construction of mini-batches for stochastic gradient descent (SGD…
We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi-supervised learning benchmark experiments, and outperforms the neural net…
Graph-based methods for anomaly detection and semi-supervised learning.
problem Detecting unusual clinical actions and anomalies in hospital data.
method Label propagation, harmonic solution, regularization, graph connectivity analysis.
result Effective anomaly detection and semi-supervised learning methods for healthcare data.
MetAL improves graph classification models with fewer labeled data.
problem Efficiently selecting unlabeled graph instances for training.
method Formulates AL as bilevel optimization, uses meta-learning to approximate model performance.
result MetAL outperforms existing AL algorithms on multiple graph datasets.
Unified taxonomy for graph representation learning.
problem Lack of unified understanding and integration of graph representation learning methods.
method Proposes a Graph Encoder Decoder Model (GRAPHEDM) to unify graph neural networks, network embedding, and graph regularization.
result Unified taxonomy and Graph Encoder Decoder Model (GRAPHEDM) for graph representation learning.
A novel approach for semi-supervised learning using regularized optimal transport.
problem Improving model performance with unlabeled data.
method Regularized optimal transport between empirical measures for affinity matrix construction, incremental label propagation, and certainty score.
result Surpasses state-of-the-art results on 12 benchmark datasets.
Study evaluates graph-based semi-supervised learning under noisy label conditions.
problem Evaluation of semi-supervised learning algorithms under noisy label conditions.
method Compared graph-based semi-supervised algorithms under varying labeled data and label noise conditions.
result Laplacian Eigenmaps performed better than label propagation under noisy conditions.
Scalings in which the graph Laplacian approaches a differential operator in the large graph limit are used to develop understanding of a number of algorithms for semi-supervised learning; in particular the extension, to this graph setting, of the probit algorithm, level set and kriging methods, are studied. Both optimi…
Paper proposes a novel GCN-based SSL algorithm to enhance node representations using contrastive and generative losses.
problem Shortage of supervision in graph-based semi-supervised learning.
method Combines contrastive and generative graph convolutional networks to enrich supervision signals.
result Improves node representations and classification results on various real-world datasets.
Bayesian neural networks enhance uncertainty estimation in graph contrastive learning.
problem Uncertainty in graph contrastive learning when labeled data is scarce.
method Variational Bayesian neural networks for uncertainty estimation.
result Improved uncertainty estimation and downstream performance on semi-supervised node-classification tasks.
Graph Convolutional Networks (GCNs) have shown significant improvements in semi-supervised learning on graph-structured data. Concurrently, unsupervised learning of graph embeddings has benefited from the information contained in random walks. In this paper, we propose a model: Network of GCNs (N-GCN), which marries th…
Protein function prediction is the important problem in modern biology. In this paper, the un-normalized, symmetric normalized, and random walk graph Laplacian based semi-supervised learning methods will be applied to the integrated network combined from multiple networks to predict the functions of all yeast proteins …
Adaptive, sparse graphs improve learning performance.
problem Inappropriate kNN for varying sampling density or noise.
method Quadratically regularised optimal transport.
result Graphs outperform kNN in learning applications.
Graph-based kernels improve GP performance on graph data.
problem Improving Gaussian process performance on graph-structured data.
method Introduced graph neural network-inspired kernels into Gaussian processes.
result Graph convolutional networks are equivalent to certain GP kernels when infinitely wide.
S2MAM improves semi-supervised learning by selecting relevant variables and updating similarity metrics.
problem Joint learning from labeled and unlabeled data with geometric structure.
method Bilevel optimization scheme for automatic variable selection and similarity matrix update.
result The proposed S2MAM achieves robust and interpretable predictions.